Search Results for author:
Found 8 papers, 2 papers with code
An Anti-fraud System for Car Insurance Claim Based on Visual Evidence
Both local and global deep features are extracted using VGG model\cite{Simonyan14c}, which are fused later for more robust system performance.
Autoencoder Regularized Network For Driving Style Representation Learning
In this paper, we study learning generalized driving style representations from automobile GPS trip data.
Characterizing Driving Styles with Deep Learning
Characterizing driving styles of human drivers using vehicle sensor data, e. g., GPS, is an interesting research problem and an important real-world requirement from automotive industries.
Self-Paced Multi-Task Learning
In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL).
A Self-Paced Regularization Framework for Multi-Label Learning
In this paper, we propose a novel multi-label learning framework, called Multi-Label Self-Paced Learning (MLSPL), in an attempt to incorporate the self-paced learning strategy into multi-label learning regime.
Joint Active Learning with Feature Selection via CUR Matrix Decomposition
In particular, our method runs in one-shot without the procedure of iterative sample selection for progressive labeling.
Dynamic Structure Embedded Online Multiple-Output Regression for Stream Data
MORES can \emph{dynamically} learn the structure of the coefficients change in each update step to facilitate the model's continuous refinement.
Max-Margin based Discriminative Feature Learning
In this paper, we propose a new max-margin based discriminative feature learning method.
